Incorporating Historical Models with Adaptive Bayesian Updates

This paper considers Bayesian approaches for incorporating information from a historical model into a current analysis when the historical model includes only a subset of covariates currently of interest. The statistical challenge is two-fold. First, the parameters in the nested historical model are not generally equal to their counterparts in the larger current model, neither in value nor interpretation. Second, because the historical information will not be equally informative for all parameters in the current analysis, additional regularization may be required beyond that provided by the historical information. We propose several novel extensions of the so-called power prior that adaptively combine a prior based upon the historical information with a variance-reducing prior that shrinks parameter values toward zero. The ideas are directly motivated by our work building mortality risk prediction models for pediatric patients receiving extracorporeal membrane oxygenation, or ECMO. We have developed a model on a registry-based cohort of ECMO patients and now seek to expand this model with additional biometric measurements, not available in the registry, collected on a small auxiliary cohort. Our adaptive priors are able to leverage the efficiency of the original model and identify novel mortality risk factors. We support this with a simulation study, which demonstrates the potential for efficiency gains in estimation under a variety of scenarios

Accounting for established predictors with the multistep elastic net

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151898/1/sim8313.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151898/2/sim8313_am.pd

Default Priors for the Intercept Parameter in Logistic Regressions

In logistic regression, separation refers to the situation in which a linear combination of predictors perfectly discriminates the binary outcome. Because finite-valued maximum likelihood parameter estimates do not exist under separation, Bayesian regressions with informative shrinkage of the regression coefficients offer a suitable alternative. Little focus has been given on whether and how to shrink the intercept parameter. Based upon classical studies of separation, we argue that efficiency in estimating regression coefficients may vary with the intercept prior. We adapt alternative prior distributions for the intercept that downweight implausibly extreme regions of the parameter space rendering less sensitivity to separation. Through simulation and the analysis of exemplar datasets, we quantify differences across priors stratified by established statistics measuring the degree of separation. Relative to diffuse priors, our recommendations generally result in more efficient estimation of the regression coefficients themselves when the data are nearly separated. They are equally efficient in non-separated datasets, making them suitable for default use. Modest differences were observed with respect to out-of-sample discrimination. Our work also highlights the interplay between priors for the intercept and the regression coefficients: numerical results are more sensitive to the choice of intercept prior when using a weakly informative prior on the regression coefficients than an informative shrinkage prior

Arterial revascularization with the right gastroepiploic artery and internal mammary arteries in 300 patients

From September 1989 to September 1992, the right gastroepiploic artery in combination with one or both internal mammary arteries was used as a graft in 300 patients who underwent coronary artery bypass grafting. The gastroepiploic artery was the primary choice in preference to the saphenous vein. The study comprised 263 men and 37 women, ranging in age from 31 to 77 years (median age 59 years). Thirty-nine patients (13%) underwent previous bypass procedures with autologous vein grafts. In 17 patients (5.7%) the gastroepiploic artery was used as a single graft. In 150 patients (50%) the gastroepiploic artery in conjunction with one internal mammary artery was used (in 6 patients combined with a vein graft). In 133 patients (44.3%) the gastroepiploic artery was used with both internal mammary arteries. Revascularization in nine patients (3%) was combined with another cardiac procedure; three aortic valve replacements, two mitral valve repairs, and four resections of a left ventricular aneurysm. Ten patients died in the hospital (3.3%; 70% confidence limits 2.3% to 4.8%); two of these patients had an infarction in the area revascularized by the gastroepiploic artery. At late follow-up, 0.5 to 39 months (mean 14 months) after the operation, we found no mortality. One patient with an occluded gastroepiploic artery graft underwent reoperation with the use of the right internal mammary artery. One patient underwent percutaneous transluminal coronary angioplasty of the right coronary artery after occlusion of the gastroepiploic artery. Elective recatheterization was done in 88 patients 1 to 25 months after operation (mean 10 months). Graft patency in gastroepiploic artery grafts increased steadily from 77% in the first semester of the study to 95% in the fourth semester and then equaled the patency of the internal mammary artery grafts (97%), which was almost constant during the whole period. We conclude that patency of the gastroepiploic artery was initially related to a ''learning curve'' but eventually equaled that of the internal mammary artery grafts. Furthermore, the gastroepiploic artery may well be the graft of choice in conjunction with the internal mammary arteries

Generalized Conformal Quantum Mechanics of D0-brane

We study the generalized conformal quantum mechanics of the probe D0-brane in the near horizon background of the bound state of source D0-branes. We elaborate on the relationship of such model to the M theory in the light cone frame.Comment: 14 pages, RevTeX, revised version with added references to appear in Phys. Rev.

The effects of treatment as usual versus a computerized clinical decision aid on shared decision-making in the treatment of psychotic disorders

Background and objectives: People with psychotic disorders can experience a lack of active involvement in their decisional process. Clinical decision aids are shared decision-making tools which are currently rarely used in mental healthcare. We examined the effects of Treatment E-Assist (TREAT), a computerized clinical decision aid in psychosis care, on shared decision-making and satisfaction with consultations as assessed by patients. Methods: A total of 187 patients with a psychotic disorder participated. They received either treatment as usual in the first phase (TAU1), TREAT in the second phase or treatment as usual in the third phase of the trial (TAU2). The Decisional Conflict Scale was used as primary outcome measure for shared decision-making and patient satisfaction as secondary outcome. Results: A linear mixed model analysis found no significant effects between TAU 1 (β = −0.54, SE = 2.01, p = 0.80) and TAU 2 (β = −1.66, SE = 2.63, p = 0.53) compared to TREAT on shared decision-making. High patient rated satisfaction with the consultations was found with no significant differences between TAU 1 (β = 1.48, SE = 1.14, p = 0.20) and TAU 2 (β = 2.26, SE = 1.33, p = 0.09) compared to TREAT. Conclusion: We expected TREAT to enhance shared decision-making without decreasing satisfaction with consultations. However, no significant differences on shared decision-making or satisfaction with consultations were found. Our findings suggest that TREAT is safe to implement in psychosis care, but more research is needed to fully understand its effects on the decisional process.</p